SoroushVahidi/scidocs huggingface dataset:
An Improved Approach for Text-Independent Speaker Recognition
(2016)

This paper presents new Speaker Identification and Speaker Verification systems based on the use of new feature vectors extracted from the speech signal. The proposed structure combine between the most successful Mel Frequency Cepstral Coefficients and new features which are the Short Time Zero Crossing Rate of the signal. A comparison between speaker recognition systems based on Gaussian mixture models using the well known Mel Frequency Cepstral Coefficients and the novel systems based on the use of a combination between both reduced Mel Frequency Cepstral Coefficients features vectors and Short Time Zero Crossing Rate features is given. This comparison proves that the use of the new reduced feature vectors help to improve the system’s performance and also help to reduce the time and memory complexity of the system which is required for realistic applications that suffer from computational resource limitation. The experiments were performed on speakers from TIMIT database for different training durations. The suggested systems performances are evaluated against the baseline systems. The increase of the proposed systems performances are well observed for identification experiments and the decrease of Equal Error Rates are also remarkable for verification experiments. Experimental results demonstrate the effectiveness of the new approach which avoids the use of more complex algorithms or the combination of different approaches requiring lengthy calculation. Keywords—GMM; speaker verification; speaker recognition; speaker identification
Simple topologies of PWM AC-AC converters
(2003)

This letter proposes a new family of simple topologies of PWM AC-AC converters with minimal switches. With extension from the basic DC-DC converters, a series of AC-AC converters such as buck, boost, buck-boost, Cuk, and isolated converters are obtained. By PWM duty ratio control, they become a "solid-state transformer" with a continuously variable turns ratio. All the proposed AC-AC converters in this paper employ only two switches. Compared to the existing circuits that use six switches or more, they can reduce cost and improve reliability. The operating principle and control method of the proposed topologies are presented. Analysis and simulation results are given using the Cuk AC-AC converter as an example. The analysis can be easily extended to other converters of the proposed family.
The MOS 36-Item Short-Form Health Survey (SF-36): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs.
(1992)

Cross-sectional data from the Medical Outcomes Study (MOS) were analyzed to test the validity of the MOS 36-Item Short-Form Health Survey (SF-36) scales as measures of physical and mental health constructs. Results from traditional psychometric and clinical tests of validity were compared. Principal components analysis was used to test for hypothesized physical and mental health dimensions. For purposes of clinical tests of validity, clinical criteria defined mutually exclusive adult patient groups differing in severity of medical and psychiatric conditions. Scales shown in the components analysis to primarily measure physical health (physical functioning and role limitations-physical) best distinguished groups differing in severity of chronic medical condition and had the most pure physical health interpretation. Scales shown to primarily measure mental health (mental health and role limitations-emotional) best distinguished groups differing in the presence and severity of psychiatric disorders and had the most pure mental health interpretation. The social functioning, vitality, and general health perceptions scales measured both physical and mental health components and, thus, had the most complex interpretation. These results are useful in establishing guidelines for the interpretation of each scale and in documenting the size of differences between clinical groups that should be considered very large.
Arrangements for Information Technology Governance: A Theory of Multiple Contingencies
(1999)

Business world today identifies a need of IT use. It is clear that IT is transforming the business. Since IT offers both opportunities and threats, effective IT governance is required to deliver IT benefits. Enterprises have to understand this issue deeply in wider business perspective in order to successfully implement IT. Effective IT governance can be achieved through enterprise’s ability in identifying, analyzing and dealing with all related factors influencing IT governance arrangements. According to theory of multiple contingencies, an enterprise is subject to impact of some contingency forces by strengthening, weakening, or overriding their mutual effects on the IT governance mode. This paper is a critique about Sambamurthy and Zmud’s research to examine IT governance mode based on this theory in relation to effective IT governance and management. They believe that this approach can be used to explain many different IT governance mode phenomena.
Examining the effects of mass media campaign exposure and interpersonal discussions on youth's drug use: the mediating role of visiting pro-drug websites.
(2013)

To extend past research on interpersonal communication and campaign effects, we hypothesized that anti-drug mass media campaign message exposure indirectly affects visiting anti- and pro-drug websites through targeted parent-child and friend-to-friend communication against drugs, as well as through having drug-related discussions during organized group activities. Second, we posited that engaging in anti-drug interpersonal communication indirectly affects adolescents' drug use through two intervening variables: visiting anti-drug websites and visiting pro-drug websites. Using self-reported longitudinal data from 2,749 youth, we found that as youth reported higher levels of anti-drug mass media campaign message exposure, they were more likely to talk to friends about the bad consequences of drugs, how to avoid drugs, and anti-drug ads. In turn, however, they were more likely to visit pro-drug websites, and subsequently, to smoke cigarettes.
Experimental results for artificial intelligence-based self-organized 5G networks
(2017)

Until now, mobile networks are still managed in a manual and semi-automatic manner, which are costly and time-consuming. For the forthcoming Fifth Generation (5G) system, its large-scale, heterogeneous, software-defined and virtualized infrastructure simply become unmanageable if no innovative managing paradigm is applied. Recently, artificial intelligence is proposed to be applied in the 5G system to realize intelligent management and highly self-organized networks. In this paper, proof-of-concept experiments, including the setup of an intelligent 5G testbed, its closed-loop control and enabling algorithms, are presented. The experimental results reveal that applying artificial intelligence to wireless network management is both feasible and effective.
Programming algorithms for multilevel phase-change memory
(2011)

Phase-change memory (PCM) has emerged as one among the most promising technologies for next-generation nonvolatile solid-state memory. Multilevel storage, namely storage of non-binary information in a memory cell, is a key factor for reducing the total cost-per-bit and thus increasing the competitiveness of PCM technology in the nonvolatile memory market. In this paper, we present a family of advanced programming schemes for multilevel storage in PCM. The proposed schemes are based on iterative write-and-verify algorithms that exploit the unique programming characteristics of PCM in order to achieve significant improvements in resistance-level packing density, robustness to cell variability, programming latency, energy-per-bit and cell storage capacity. Experimental results from PCM test-arrays are presented to validate the proposed programming schemes. In addition, the reliability issues of multilevel PCM in terms of resistance drift and read noise are discussed.
Analysis of Human Electrocardiogram for Biometric Recognition
(2008)

Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric trait, the human heartbeat, can be used for identity recognition. Existing solutions for biometric recognition from electrocardiogram (ECG) signals are based on temporal and amplitude distances between detected fiducial points. Such methods rely heavily on the accuracy of fiducial detection, which is still an open problem due to the difficulty in exact localization of wave boundaries. This paper presents a systematic analysis for human identification from ECG data. A fiducial-detection-based framework that incorporates analytic and appearance attributes is first introduced. The appearance-based approach needs detection of one fiducial point only. Further, to completely relax the detection of fiducial points, a new approach based on autocorrelation (AC) in conjunction with discrete cosine transform (DCT) is proposed. Experimentation demonstrates that the AC/DCT method produces comparable recognition accuracy with the fiducial-detection-based approach.
Within-class covariance normalization for SVM-based speaker recognition
(2006)

This paper extends the within-class covariance normalizat ion (WCCN) technique described in [1, 2] for training generaliz ed linear kernels. We describe a practical procedure for apply ing WCCN to an SVM-based speaker recognition system where the input feature vectors reside in a high-dimensional space. O ur approach involves using principal component analysis (PCA) t o split the original feature space into two subspaces: a low-dimens ional “PCA space” and a high-dimensional “PCA-complement space. ” After performing WCCN in the PCA space, we concatenate the resulting feature vectors with a weighted version of their P CAcomplements. When applied to a state-of-the-art MLLR-SVM speaker recognition system, this approach achieves improv ements of up to 22% in EER and 28% in minimum decision cost function (DCF) over our previous baseline. We also achieve substanti al improvements over an MLLR-SVM system that performs WCCN in the PCA space but discards the PCA-complement.
ECG Biometric Recognition: A Comparative Analysis
(2012)

The electrocardiogram (ECG) is an emerging biometric modality that has seen about 13 years of development in peer-reviewed literature, and as such deserves a systematic review and discussion of the associated methods and findings. In this paper, we review most of the techniques that have been applied to the use of the electrocardiogram for biometric recognition. In particular, we categorize the methodologies based on the features and the classification schemes. Finally, a comparative analysis of the authentication performance of a few of the ECG biometric systems is presented, using our inhouse database. The comparative study includes the cases where training and testing data come from the same and different sessions (days). The authentication results show that most of the algorithms that have been proposed for ECG-based biometrics perform well when the training and testing data come from the same session. However, when training and testing data come from different sessions, a performance degradation occurs. Multiple training sessions were incorporated to diminish the loss in performance. That notwithstanding, only a few of the proposed ECG recognition algorithms appear to be able to support performance improvement due to multiple training sessions. Only three of these algorithms produced equal error rates (EERs) in the single digits, including an EER of 5.5% using a method proposed by us.
ECG to identify individuals
(2005)

The electrocardiogram (ECG also called EKG) trace expresses cardiac features that are unique to an individual. The ECG processing followed a logical series of experiments with quantifiable metrics. Data filters were designed based upon the observed noise sources. Fiducial points were identified on the filtered data and extracted digitally for each heartbeat. From the fiducial points, stable features were computed that characterize the uniqueness of an individual. The tests show that the extracted features are independent of sensor location, invariant to the individual’s state of anxiety, and unique to an individual. 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
ECG analysis: a new approach in human identification
(2001)

In this paper, a new approach in human identification is investigated. For this purpose, a standard 12-lead electrocardiogram (ECG) recorded during rest is used. Selected features extracted from the ECG are used to identify a person in a predetermined group. Multivariate analysis is used for the identification task. Experiments show that it is possible to identify a person by features extracted from one lead only. Hence, only three electrodes have to be attached on the person to be identified. This makes the method applicable without too much effort.
Development of an Airborne Ground Penetrating Radar System: Antenna Design, Laboratory Experiment, and Numerical Simulation
(2014)

A stepped-frequency continuous wave (SFCW) airborne ground penetrating radar (GPR) system was built using a vector network analyzer (VNA), an optical-to-electric ( \mmb O/\mmb E) converter, an electric-to-optical ( \mmb E/\mmb O) converter, and the resistively loaded planer dipole antennas. The resistively loaded antennas are based on the Altshuler discrete resistive loading approach. The distance from the loading point to the feeding point and the resistance has been optimized; in addition, a balun at the feeding point was introduced to convert the unbalanced signal from the coax to balanced signal. Test results show that the manufactured antennas have a wide bandwidth frequency range from 55 to 275 MHz. A laboratory experiment using the developed system was conducted in a building with high ceilings. The results registered all basic features including the ground, sub-ground surface, and the lamp hanging from the ceiling. Numerical modeling by finite-difference time-domain (FDTD) method using two-dimensional (2-D) transverse electric (TE) verifies the presence of these registered features in the measured airborne data. A bedrock outcrop in ice sheet model is reconstructed based on a piece of real airborne GPR data, the modeling profile results show excellent consistent with the observed data. The capability of airborne GPR detection of subsurface features is clearly validated.
Intrusion Detection in the RPL-connected 6LoWPAN Networks
(2017)

The interconnectivity of 6LoWPAN networks with the Internet raises serious security concerns, as constrained 6LoWPAN devices are accessible anywhere from the untrusted global Internet. Also, 6LoWPAN devices are mostly deployed in unattended environments, hence easy to capture and clone. Despite that state of the art crypto solutions provide information security, IPv6 enabled smart objects are vulnerable to attacks from outside and inside 6LoWPAN networks that are aimed to disrupt networks. This paper attempts to identify intrusions aimed to disrupt the Routing Protocol for Low-Power and Lossy Networks (RPL).In order to improve the security within 6LoWPAN networks, we extend SVELTE, an intrusion detection system for the Internet of Things, with an intrusion detection module that uses the ETX (Expected Transmissions) metric. In RPL, ETX is a link reliability metric and monitoring the ETX value can prevent an intruder from actively engaging 6LoWPAN nodes in malicious activities. We also propose geographic hints to identify malicious nodes that conduct attacks against ETX-based networks. We implement these extensions in the Contiki OS and evaluate them using the Cooja simulator.
Shrinkable, stiffness-controllable soft manipulator based on a bio-inspired antagonistic actuation principle
(2014)

This paper explores a new hybrid actuation principle combining pneumatic and tendon-driven actuators for a soft robotic manipulator. The fusion of these two actuation principles leads to an overall antagonistic actuation mechanism whereby pneumatic actuation opposes tendon actuation - a mechanism commonly found in animals where muscles can oppose each other to vary joint stiffness. We are taking especially inspiration from the octopus who belongs to the class of Cephalopoda; the octopus uses its longitudinal and transversal muscles in its arms to achieve varied motion patterns; activating both sets of muscles, the octopus can control the arm stiffness over a wide range. Our approach mimics this behavior and achieves comparable motion patterns, including bending, elongation and stiffening. The proposed method combines the advantages of tendon-driven and pneumatic actuated systems and goes beyond what current soft, flexible robots can achieve: because the new robot structure is effectively an inflatable, sleeve, it can be pumped up to its fully inflated volume and, also, completely deflated and shrunk. Since, in the deflated state, it comprises just its outer “skin” and tendons, the robot can be compressed to a very small size, many times smaller when compared to its fully-inflated state. In this paper, we describe the mechanical structure of the soft manipulator. Proof-of-concept experiments focus on the robot's ability to bend, to morph from completely shrunk to entirely inflated as well as to vary its stiffness.
Fast and Accurate Crop and Weed Identification with Summarized Train Sets for Precision Agriculture
(2016)

In this paper we present a perception system for agriculture robotics that enables an unmanned ground vehicle (UGV) equipped with a multi spectral camera to automatically perform the crop/weed detection and classification tasks in real-time. Our approach exploits a pipeline that includes two different convolutional neural networks (CNNs) applied to the input RGB+near infra-red (NIR) images. A lightweight CNN is used to perform a fast and robust, pixelwise, binary image segmentation, in order to extract the pixels that represent projections of 3D points that belong to green vegetation. A deeper CNN is then used to classify the extracted pixels between the crop and weed classes. A further important contribution of this work is a novel unsupervised dataset summarization algorithm that automatically selects from a large dataset the most informative subsets that better describe the original one. This enables to streamline and speed-up the manual dataset labeling process, otherwise extremely time consuming, while preserving good classification performances. Experiments performed on different datasets taken from a real farm robot confirm the effectiveness of our approach.
Osmotic Heat Engine Using Thermally Responsive Ionic Liquids.
(2017)

The osmotic heat engine (OHE) is a promising technology for converting low grade heat to electricity. Most of the existing studies have focused on thermolytic salt systems. Herein, for the first time, we proposed to use thermally responsive ionic liquids (TRIL) that have either an upper critical solution temperature (UCST) or lower critical solution temperature (LCST) type of phase behavior as novel thermolytic osmotic agents. Closed-loop TRIL-OHEs were designed based on these unique phase behaviors to convert low grade heat to work or electricity. Experimental studies using two UCST-type TRILs, protonated betaine bis(trifluoromethyl sulfonyl)imide ([Hbet][Tf2N]) and choline bis(trifluoromethylsulfonyl)imide ([choline][Tf2N]) showed that (1) the specific energy of the TRIL-OHE system could reach as high as 4.0 times that of the seawater and river water system, (2) the power density measured from a commercial FO membrane reached up to 2.3 W/m2, and (3) the overall energy efficiency reached up to 2.6% or 18% of the Carnot efficiency at no heat recovery and up to 10.5% or 71% of the Carnet efficiency at 70% heat recovery. All of these results clearly demonstrated the great potential of using TRILs as novel osmotic agents to design high efficient OHEs for recovery of low grade thermal energy to work or electricity.
Study of Viola-Jones Real Time Face Detector
(2016)

Face detection has been one of the most studied topics in computer vision literature. Given an arbitrary image, the goal of face detection is to determine whether or not there are any faces in the image and, if present, return the image location and extent of each face. While this appears to be a trivial task for human beings, it is a very challenging task for computers. The difficulty associated with face detection can be attributed to many variations in scale, location, view point, illumination, occlusions, etc. Although there have been hundreds of reported approaches to face detection, if one were asked to name a single face detection algorithm that has the most impact in recent decades, it will most likely be the Viola and Jones face detection, which is capable of processing images extremely rapidly and achieve high detection rates. This project is going to study and understand the Viola-Jones algorithm by implementing the whole detection framework and based on the implementation, conduct experiment to hopefully further improve the performance.
Augmented Reality through Wearable Computing
(1997)

Wearable computing moves computation from the desktop to the user. We are forming a community of networked, wearable-computer users to explore, over a long period, the augmented realities that these systems can provide. By adapting its behavior to the user's changing environment, a body-worn computer can assist the user more intelligently, consistently, and continuously than a desktop system. A text-based augmented reality, the Remembrance Agent, is presented to illustrate this approach. Video cameras are used both to warp the visual input (mediated reality) and to sense the user's world for graphical overlay. With a camera, the computer could track the user's finger to act as the system's mouse; perform face recognition; and detect passive objects to overlay 2.5D and 3D graphics onto the real world. Additional apparatus such as audio systems, infrared beacons for sensing location, and biosensors for learning about the wearer's affect are described. With the use of input from these interface devices and sensors, a long-term goal of this project is to model the user's actions, anticipate his or her needs, and perform a seamless interaction between the virtual and physical environments.
Visual-Interactive Preprocessing of Time Series Data
(2012)

Time series data is an important data type in many different application scenarios. Consequently, there are a great variety of approaches for analyzing time series data. Within these approaches different strategies for cleaning, segmenting, representing, normalizing, comparing, and aggregating time series data can be found. When combining these operations, the time series analysis preprocessing workflow has many degrees of freedom. To define an appropriate preprocessing pipeline, the knowledge of experts coming from the application domain has to be included into the design process. Unfortunately, these experts often cannot estimate the effects of the chosen preprocessing algorithms and their parameterizations on the time series. We introduce a system for the visual-interactive exploitation of the preprocessing parameter space. In contrast to ‘black box’-driven approaches designed by computer scientists based on the requirements of domain experts, our system allows these experts to visual-interactively compose time series preprocessing pipelines by themselves. Visual support is provided to choose the right order and parameterization of the preprocessing steps. We demonstrate the usability of our approach with a case study from the digital library domain, in which time-oriented scientific research data has to be preprocessed to realize a visual search and analysis application.